Machine Learning with scikit-learn

In this lab, you are tasked with building a supervised machine learning pipeline to predict the median values of owner-occupied housing in USD 1000, denoted as MEDV. You will utilize the Boston dataset, which contains a set of different features that are used to predict the MEDV target variable. Here, you will be guided with a hands-on exercise on data preprocessing, fitting, and evaluation of a regression model.

To get the most from this lab, it is recommended to have confidence and exposure to the following libraries: `pandas`, `matplotlib`, and `scikit-learn`.

Ensure that you've completed the following courses before completing this lab.

• Building a Machine Learning pipeline with scikit-learn: part 1
• Building a Machine Learning pipeline with scikit-learn: part 2

Learning Objectives

Upon completion of this lab, you will be able to:

• Build a standard machine learning pipeline with scikit-learn
• Scale a dataset using the StandardScaler transformer
• Train a Ridge Regression Model
• Fit a scikit-learn Pipeline object with a GridSearchCV.

Intended Audience

This lab is intended for:

• Those interested in performing machine learning with Python.
• Anyone involved in data science pipelines.

Prerequisites

You should possess:

• An intermediate understanding of Python.
• Basic knowledge of the following libraries: pandas, scikit-learn, matplotlib, and seaborn.

Updates

August 18th, 2025 - Updated links in description

August 8th, 2024 - Resolved Jupyter Notebook issue